# Perplexity 发布基于 GLM 5.2 微调的编排模型，性能接近前沿且成本仅为 Opus 三分之一

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-22 14:20
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmrvp3wll0016bib2axgfzwbl
- 原文链接：https://x.com/rohanpaul_ai/status/2079813927119257689

## AI 摘要

Perplexity 在 Perplexity Computer 中发布了一款基于 GLM 5.2 微调的编排模型研究预览。该模型在 Terminal-Bench 2.1 上得分 80，超越 Opus 4.8（76）和 GPT-5.5（78），平均任务成本仅为 Opus 的三分之一（0.344x）。

## 正文

Perplexity just shipped an agent model/orchestrator model， that matches near-frontier performance at one-third the cost of Opus.

"GLM 5.2 + advisor"， its a tuned version of GLM 5.2， retrained specifically for Perplexity Computer， its agent environment.

Running frontier models like Opus 4.8 or GPT-5.5 on every task gets expensive fast.

So the cheaper base handles most work， then escalates to a stronger model only when it needs help.

That escalation runs through a built-in advisor tool that decides when the extra horsepower is worth calling.

On Terminal-Bench 2.1 the setup scores 80， ahead of Opus 4.8 at 76 and GPT-5.5 at 78.

But cost is where the numbers turn.

On WANDR the advisor build runs at 2.1x the GLM 5.2 baseline， while Opus burns 6.1x.

Averaged across benchmarks， it costs roughly half what Opus does per task.

Perplexity hosts it in the U.S. on Nvidia B200 GPUs， with fuller benchmarks promised soon.

The interesting move is treating a strong model as an on-call consultant with an escalation logic， not the default worker.

### 引用推文

> Perplexity：We're releasing a research preview of a new orchestrator model in Perplexity Computer. The model is an adapted version of GLM 5.2, post-trained for the Computer...